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    Data mining for business analytics : concepts, techniques, and applications in JMP Pro / Galit Shmueli, Peter C. Bruce, Mia L. Stephens, Nitin R. Patel.

    By: Shmueli, Galit, 1971- [author.]Contributor(s): Bruce, Peter C, 1953- [author.] | Stephens, Mia L [author.] | Patel, Nitin R. (Nitin Ratilal) [author.]Material type: TextTextPublisher: Hoboken, New Jersey : Wiley, 2017Description: xxii, 442 pages ; 27 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781118877432 (cloth); 1118877438 (cloth)Subject(s): JMP (Computer file) | JMP (Computer file) | Business mathematics -- Computer programs | Business -- Data processing | Data mining | Business -- Data processing | Business mathematics -- Computer programs | Data mining | Data Mining | Business IntelligenceDDC classification: 006.3/12 LOC classification: HF5691 | .S43245 2017
    Contents:
    Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- K-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Cases.
    Summary: Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter; End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material; Data-rich case studies to illustrate various applications of data mining techniques; A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.--Publisher website.
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    Item type Current library Call number Copy number Status Notes Date due Barcode
    Books Books Female Library
    		HF5691 .S43245 2017 (Browse shelf (Opens below)) 1 Available STACKS 51952000329923
    Books Books Main Library
    		HF5691 .S43245 2017 (Browse shelf (Opens below)) 1 Available STACKS 51952000329930

    Includes index.

    Includes bibliographical references and index.

    Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- K-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Cases.

    Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter; End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material; Data-rich case studies to illustrate various applications of data mining techniques; A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.--Publisher website.

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